comparison of subaquatic digital elevation models … … · methods for digital elevation model...

COMPARISON OF SUBAQUATIC DIGITAL ELEVATION MODELS FROM AIRBORNE

LASER SCANNING AND IMAGERY

C. Mulsow 1*, G. Mandlburger2,3, H.-G. Maas 1

1 Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden –

(christian.mulsow, hans-gerd.maas)@tu-dresden.de 2Department of Geodesy and Geoinformation, TU Wien: [email protected] 3Institute of Photogrammetry, University of Stuttgart: [email protected]

Commission II, WGII/9

KEY WORDS: bathymetry, laser scanning, DEM, underwater, multi-media photogrammetry, bundle-adjustment

ABSTRACT:

The paper describes and compares the workflows and results of generating digital elevation models (DEMs) of underwater areas from

airborne laser scanning and aerial stereo images. Based on a combined laser scanning/image data set of an artificial lake, both methods

are described and pros/cons are highlighted. The authors focus on the final results, especially on accuracy, completeness and spatial

resolution of the underwater DEM’s. Further, practical aspects of processing and complexity of both methods are highlighted too.

1. MOTIVATION

Lasers scanning and photogrammetry are two well established

methods for Digital Elevation Model (DEM) acquisition in all

scales. Before the advent of laser scanning systems, stereo

photogrammetry was the method of choice for this task. Over the

past decades, laser scanning was established as an effective tool

for DEM acquisition. Especially the multi-target technology as

well as full-wave-form analysis (Pfeifer et al., 2015) tipped the

balance in favour of laser scanning. Nevertheless, the

introduction of highly automated techniques for image

orientation (e.g. Structure from Motion, SfM) and dense pixel-

wise matching led to a renaissance of photogrammetry in the last

decade. As shown in several projects (Mandlburger et al., 2017),

both methods can be applied complementary in order to improve

the final results.

Airborne laser bathymetry established its stand as a standard

method for capturing submerged topography of shallow water

Figure 1. Study area Autobahnsee– a manmade freshwater lake

near Augsburg, Germany.

* Corresponding author

bodies, like riverbanks or shore lines (Wozencraft, Lillycrop,

2003, Kinzel et al., 2013, Song et al., 2015). For the same task,

photogrammetry can be used in the classical way.

Both methods have to take the refraction into account while

measuring through refracting surfaces - in this case water. Crucial

for modelling the refraction is precise knowledge of the water

level height and orientation as well as the refractive index. The

refractive index for a specific water body can be estimated from

salinity and temperature (Quan, Fry, 1995). More demanding is

the determination of the shape of the water surface, especially in

wavy conditions. Further, effects of water flow, causing local

sinks and stagnations, have to be taken into account.

Nevertheless, in the case of calm water, the water surface can be

approximated well as a horizontal plane. Therefore, only the

water level height has to be determined. This can be done via

local gauge measurement or simultaneously within data

processing.

Figure 2. Block layout with control points (yellow) and extracted

point cloud (screenshot Pix4 Mapper)

670 m

420

m

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume V-2-2020, 2020 XXIV ISPRS Congress (2020 edition)

This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-V-2-2020-671-2020 | © Authors 2020. CC BY 4.0 License.

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2. DATA SET

Laser and image data were acquired simultaneously on 9 April

2018 during a flight over a lake in the flood plain of the river

Lech near Augsburg, Bavaria, Germany (see Figure 1). The main

substrate on the lake ground is coarse grained gravel. The

airplane was equipped with a topo-bathymetrical laser scanner

(Riegl VQ-880-G) together with two 100 Mpix cameras (IGI

DigiCAM 100). One was a standard RGB-camera, while the

second one was operated in a monochromatic mode together with

a Coastal-Blue filter (λ=400-460 nm) (Mandlburger et al., 2018).

Altogether four stripes were acquired at flight speed of 100 knots

(50 m/s).

2.1 Image Data

The image block consists of 125 images captured from two

different flying altitudes; 64 images from 610m resulting in a

ground sampe distance (GSD) of 5.6 cm and 61 images from

450 m with a GSD of 4.2 cm. For each height, two strips were

acquired with an overlap along track of 90% and cross track of

60% (see Figure 2). Therefore, the redundancy is quite high, e.g.

the centre of the area was captured in 40 images. As mentioned

before, the used camera was a IGI DigiCAM 100 medium format

camera with a resolution of 10608x8708 Pixel (100 Mpx)

together with a 50 mm lens. The camera itself was developed

from a PhaseOne iXU-RS 1000 (Mandlburger et al., 2018). For

the tests, the RGB images were processed exclusively in favour

of the Coastal-Blue data, because of the more suitable

characteristics for automatic image analysis (Mandlburger et al.,

2018). For geo-referencing GNSS and INS data were acquired

during the flight. Additionally, 10 ground control point targets

were installed and measured via RTK-GNSS (see Figure 2).

2.1.1 Image Orientation

In a first step, the whole block was processed in Pix4D Mapper

(www.pix4d.com) with geo-referencing based on control points.

In the same software, a point cloud together with an orthophoto

mosaic was generated. While the DEMs can be assumed correct

for land areas, the submerged surface points were falsified by

refraction. Generally, water depths in these areas are

underestimated when refraction is not taken into account (Maas,

2015). So, for a correct calculation of point heights a

compensation of refraction is essential. Thanks to previous works

in this field, a dedicated software was available. This multi-media

bundle adjustment software was developed at the Institute of

Photogrammetry and Remote Sensing, Technische Universität

Dresden (Mulsow, 2010).

Processing of the whole block in MatchAT (Trimble/ Inpho)

provided initial values for interior and exterior orientation as well

as image point coordinates of control points (manual) and tie

points (automated), respectively. For classification of submerged

and land points, the heights of the 3D tie point coordinates were

used – with the water level height as discriminator. From manual

image measurements of the shore line, a water level of 509.5 m

could be determined. All points below this level could be

classified as submerged. Altogether, ~17.000 image

measurements of ~1000 tie points were provided for the multi-

media bundle adjustment, of which ~1900 image coordinates

related to ~120 submerged tie points.

Experiences on similar projects had shown that a reliable

determination of the water level inside the bundle process is not

possible from block of nadir images (Mulsow, 2018). Therefore,

the lake level was defined as a horizontal plane with a fixed

height of 509.5 m. A reliable determination of refractive surfaces

inside a bundle adjustment requires convergent images as well as

relatively low impact angles of image rays on to the refractive

surface. As shown in (Mulsow, Maas, 2014) for close range

image blocks with proper block geometry, the surface parameters

as well as refractive indices can be determined with high

accuracy.

Bundle block adjustment was done in two parameter

configurations with different treatment of underwater points:

I. Simultaneous determination of all unknowns

(interior and exterior orientations), underwater

points treated as single-media points (without

modelling of refraction)

II. Simultaneous determination of all unknowns

(interior and exterior orientations), underwater

points treated as multi-media points (considering

refraction)

Both adjustments were processed without gross error detection,

which was done in advance. As mentioned before, both runs were

done with the same input data – just for the underwater points the

refraction was either neglected (configuration I – conventional

bundle adjustment) or modelled (configuration II – multimedia

bundle adjustment).

From Table 1 it’s obvious that the modelling of refraction has

virtually no impact on the accuracy values (back projection error

s0, RMS of residuals of image measurements, RMS of the

standard deviations of the estimated object point coordinates). It

is noted that the RMS values reported in Table 1 are calculated

from the (a posteriori) residuals rather than from the covariance

matrix of the bundle block adjustment. The positions of tie points

are close together for both adjustments (RMS 0.1 m in x/y, note

noted in Table 1). As expected, the heights differ significantly for

submerged areas (see Figure 3).

Parameter

I – without

modelled

refraction

II – with

modelled

refraction

Back projection error s0

[px] 0.11 0.11

RMS image points land

x’ y’ [px] 0.09/0.10 0.10/0.10

RMS image points water

x’ y’ [px] 0.13/0.14 0.14/0.14

focal length ck [mm] 51.532 51.529

principal point xH [mm] 0.0042 0.0041

principal point yH [mm] 0.0287 0.0286

Radial symmetric

distortion A1 -1.486e-05 -1.490e-05

Radial symmetric

distortion A2 3.933e-09 3.953e-09

RMS tie points

land X/Y/Z [cm] 0.1/0.2/2.6 0.1/0.2/2.6

RMS tie points

water X/Y/Z [cm] 0.2/0.3/3.9 0.2/0.3/5.4

Table 1. Bundle adjustment results with and without modelling

of refraction (single-media vs. two-media).



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http://www.pix4d.com/

Figure 3. Comparison of height values of tie points against

water depth from bundle adjustment with and without

modelling of refraction.

Anyway, the exterior orientation parameters as well as camera

parameters are similar for both runs. The differences are smaller

than their accuracy, which means that a conventional bundle

adjustment is suitable for block orientation in this example. It can

be assumed, that vertical oriented image blocks showing shallow

water bodies can be processed as standard blocks.

The reason lies in the homogeneity of refraction effects:

Therefore, image rays of submerged points are refracted in a

similar way. Further, the water depth to flight height ratio is quite

small, which means that only a small part of the image ray is

refracted. In the end, both effects result in a relatively small

distance of (refracted) image rays from their adjusted

(submerged) intersection point. According to this, standard

software can be used for orientation of such image blocks

Anyway, no definitive recommendation can be given without

further investigations. For DEM extraction a dedicated

intersection routine with refraction compensation should be used

(Mandlburger, 2018). As shown in (Mulsow, 2010) such a

routine can be easily implemented, in contrast to a resection- or

bundle-adjustment software.

Table 1 (Setup II) shows a drop of accuracy of just factor 2 (RMS

from adjustment) for land and underwater points. This can be

seen as far too optimistic, because for calculation of inner

accuracy values the refraction index as well as the water surface

parameters were treated as fixed. Nevertheless, small local

variations of these parameters due to wind and temperature can

be assumed. A comparison with check points would give a more

realistic estimation of height accuracy. Unfortunately, no such

reference data were taken in the field. From similar projects

(Mulsow, 2018), a drop of accuracy of underwater points of

factor 4 (against land points) can be assumed. Therefore, a height

accuracy of 10 cm instead of 5 cm can be estimated.

2.1.2 DEM from image data

The DEM extraction follows the common procedure:

- image matching in stereo pairs

- computing of object coordinates via forward intersection.

First, image pairs were defined automatically from the low

altitude image subset. In order to achieve large intersection

angles, pairs with an overlap of ~60% were chosen. Due to high

overlap along track, a number of neighbouring images could be

neglected. The average distance of stereo partners was about 5

images. An overlap of neighbouring image pairs of 60% was

defined too. In the end, only half of the low altitude images were

used.

Stereo image pairs were rectified with normal geometry in order

to minimize y-parallaxes. In contrast to single-medium case, the

epipolar lines are not straight for underwater points due to

refraction. However, thanks to the vertical imaging direction as

well as the relatively shallow water (compared to the flying

altitude), this effect is negligible for image point matching.

The matching was implemented as a pyramid search approach,

starting from a reduced resolution of factor 5 up to the full

resolution. For each step, the image was divided into 75x50 raster

cells. For each cell, the best feature point (Harris-operator) was

found (Harris, Stephens, 1988). The Harris points were localised

in the stereo partner image and the subpixel position was

measured via Least Squares Matching (Gruen, 1985) with a patch

size of 21x21 pixel, x/y shift and scale parameter. From parallax

differences, a disparity map was created for the actual resolution

step. The disparity map was resampled for the next resolution

step and gave initial value for the following matching process.

After reaching full resolution, a final matching was done with full

parameter set in order to achieve best results and to overcome

refraction effects. Based on matching results, all object points

coordinates were computed via conventional forward

intersection.

Figure 4. Matched points on land (black) and in water (blue)

together with shore line (red)

Figure 5. Interpolated DEM from image data with orthophoto

mosaic in the background layer. The point heights are given

relatively to water level in [m].

-5

-4,5

-4

-3,5

-3

-2,5

-2

-1,5

-1

-0,5

0

-4,5 -3,5 -2,5 -1,5 -0,5 0,5

poin

t hei

ght f

rom

bun

dle

[m]

water depth [m]

with modelling of refraction without modelling of refraction



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In order to identify underwater points, the points were analysed

by their heights – points below the pre-defined water level were

considered as submerged points and vice versa. Finally, the

underwater point coordinates were computed via multimedia-

forward intersection (Mulsow, 2018).

2.1.3 Analysis of results

As mentioned before, the focus of this study laid on the DEM

quality of submerged areas. As shown in Figure 4, underwater

feature points were found mainly on the shore and on border

zones of vegetation. This illustrates the main drawback of image

based DEM generation – the dependency on (image) texture.

Therefore, homogeneous areas show either no or false

measurements. Nevertheless, for this project points up to 4 m

below water surface (max. depth 4.5 m) could be determined with

good reliability (see also Figure 5).

The precision of point coordinates is not homogenous for the

whole area. As expected, the imaging quality drops with

increasing water depth. Therefore, the precision drops with

increasing depth too. Without reference data this effect can only

be estimated.

The high overlap inside the image block (along- and cross-strip)

results in a high overlap of DEM’s from different image pairs.

Therefore, the inner precision can be estimated by comparing the

height data from overlapping DEMs. For this purpose, two

DEM’s from different strips were transferred to cubic-spline-

surfaces and rasterized to 0.5 m grids. The comparison of node

heights shows a good fit of both models (RMS 4.4 cm, average

distance in mm-level, no offset). Nevertheless, areas with only a

few feature points (low image contrast) show significant

differences (max. 22 cm). However, the precision of underwater

points given in Table 1 could be confirmed in general.

In theory, the geometric conditions for forward intersection is

more stable for land- than for underwater points due to refraction.

The image ray intersection angle in the denser medium (water)

becomes smaller (Maas, 2015), thus degrading the accuracy.

For a visual test, the surface model was intersected with the water

surface and the resulting line was projected was plotted in the

orthophoto. As seen in Figure 9, the calculated shore line follows

the real one quite closely. This especially holds true for open

areas as depicted in the detail in Figure 9, whereas larger

deviations can be observed in vegetated areas.

Figure 6. Analysis of relative precision based on height

differences (in [m]) of overlapping DEM’s from stereo pairs -

2915/2951 (stars) and 3088/3093 (circles)

3. LASER BATHYMETRY

3.1 Orientation of laser scanning data

Together with the image block, an additional data set was

acquired simultaneously by a topo-bathymetric LiDAR system

(Light Detection And Ranging). The whole dataset included the

trajectories of 4 strips (GNSS/INS), sensor data (range, angle,

calibrated amplitude, reflectivity per echo from online waveform

analysis (Pfennigbauer et. al., 2014), and several other per-point

attributes (echo number, scan angle, etc.). For registration, a

rigorous strip adjustment including time dependent trajectory

correction (Glira et al., 2015; Glira et al., 2016) was performed.

Quality control was based on the residues between the strip

heights in smooth areas. The robustly estimated standard

deviation (σMAD) of the remaining height differences between

overlapping flight strips amounted to 1.0 cm and the residuals

showed no bias after adjustment.

Absolute orientation of the laser flight block cloud was

established via rigid body transformation based on the

photogrammetrically derived point cloud by means of the

Iterative Closest Point (ICP) approach (Besl and McKay, 1992,

Glira et al., 2015). Apart from negligible rotation components,

the main part of the laser-to-image transformation was covered

by a 3d-offset (dx=-0.087 m, dy=-0.028, dz=+0.514). The

standard deviation of the unbiased residual height deviations

(mean dz=0.001 m) between the image block and the

transformed laser and block measured 0.036 m.

3.2 Determination of water surface and refraction

correction

The laser ray is refracted while travelling through the water

surface. Further, the propagation speed is reduced in water. Both

effects are formulated in the Snell’s law and depend on the

refractive indices of air (nA~1.0) and water (nW~1.33). Therefore,

the uncorrected laser points appear too deep for water bodies.

For correction of time of flight and beam refraction, a digital

model of the water surface is essential. The main advantage of

laser bathymetry is the ability to receive reflected signals from

the water surface as well as from the ground below. Because of

the high amount of specular reflection at the water surface and

the general water penetration capability of green laser radiation,

the backscattered laser signal is a mix of surface reflection and

volume scattering in the first centimetres of water column

(Guenther et al., 2000).

For determination of the water surface, the approach by

Mandlburger et al. (2013) was chosen. Based on a manually

defined initial height, the laser echoes were statistically analysed

in 10 m by 10 m cells. For each cell, the representative water

level was calculated as the 99% quantile of all height values

within each cell. On the one side this robustly eliminates potential

above surface outlier points while at the same time minimizing

the water level underestimation due to the volume backscattering.

The water surface model showed maximum undulation of 15 cm

with systematically higher elevations around the island in the

southern part of the lake. The inclusion of occasional land points

in the transition zone between water and land led to a slight water

level overestimation in the shore areas. Therefore, the maximum

water level height was limited to h=509.10 m, while the mean

height was 509.00 m. The mean laser derived water level height

is 5 cm below the value from image data.



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https://www.dict.cc/englisch-deutsch/from.html

With the water surface DEM on hand, each echo was corrected

by intersecting the laser ray with the water surface and by

calculating and applying the refraction and run-time correction

for the part of the laser beam in water. Therefore, each raw

underwater point coordinate saw corrections in position and

height. The whole correction process was carried out in the

software system OPALS (Pfeifer et al., 2014).

3.3 Filtering and DEM

After pre-processing, the point cloud was filtered for terrain

points (on land and submerged) no-terrain (low, mid, high

vegetation, buildings etc.) via hierarchical robust filtering

(Pfeifer et al., 2015). An additional classification was carried out

to separate lake floor, water surface and points in the water

column based on the water surface model. All terrain points (on

land and submerged) were used as an input for a regular DEM

with 50 cm spacing. While the laser point density of 25 points/m2

would allow a lower spacing, the effective spatial resolution is

limited by the size of the laser footprint of 50-60 cm

Beside the quality of the reconstructed flight trajectory

(GNSS/INS) and the accuracy of the scanning device, the

uncertainty of water surface orientation and height has to be taken

into account for estimation of accuracy potential of the derived

point cloud. From similar projects, a height accuracy of at least

10 cm can be assumed. Due to redundant coverage with data from

4 overlapping flight strips, the relative DEM precision can

expected to better by a factor of 2 (5 cm).

Figure 7. Interpolated DEM from laser scanner data.

Figure 8. Height differences over in interval of ±0.2m between

laser- and image-DEM’s.

4. COMPARISON OF RESULTS

The comparison of DEM’s from both sources showed a high

degree of similarity for vegetation free shore areas as well as for

zones with sufficient texture (see Figure 8). Therefore,

discrepancies larger than 20 cm were excluded in the analysis.

The overall coverage of laser DEM is significantly higher than

image based DEM. From this, the advantage of active method

(laser) against passive image based when capturing low-texture

areas becomes obvious.

Due to the lack of independent reference data, the absolute

accuracy of point coordinates below water level could not be

assessed However, comparison of the shore lines independently

derived from both DEMs and water surface models allow a visual

estimation of accuracy. The shore line derived from the laser

DEM follows the real one (see Figure 9) in the range of a few

decimetres. It has to be kept in mind, that the gradient of the shore

zone is quite small and therefore the same applies for the

intersection angle with the water surface. The same can be

observed for the image DEM for vegetation free shore zones. By

contrast, shore line sections hidden under vegetation area shifted

in direction of the lake. This highlights the main advantage of

laser based DEM extraction – the multi-target technology in

general and as full waveform analyses in particular allow a look

under vegetation.

Figure 9. Shore lines calculated from intersection of water

surface with image-DEM (blue) and laser-DEM (red).

5. CONCLUSION AND OUTLOOK

Both methods – laser bathymetry and two-media

photogrammetry – are competitive tools for acquisition of

underwater topography. Nevertheless, laser scanning as well as

imagery have their pros and cons regarding complexity, accuracy

and completeness of the final result. As for all optical

measurement techniques, the optical characteristics of passed

media, in this case air and, above all, water, are crucial for

measurement quality. Both methods are strongly influenced by

dispersion and turbidity. When it comes to accuracy, a clear



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overall recommendation can’t be given, because of the similar

inner accuracy (5 cm vs. 4.4 cm, comparison of overlapping

strips and models). As mentioned before, laser scanning is

independent from texture like photogrammetry. Further, the full

waveform analysis allows a look under vegetation to a certain

degree. On the other hand, the technical effort as well as

complexity is far lower when using cameras for data capture.

During image data processing, some possible improvements

could be identified. When planning such measurement

campaigns, the requirements of two-media photogrammetry

should be taken into account. In order to achieve larger

intersection angles, optics with large opening angles should be

used. Oblique imagery could further improve the intersection

geometry. However, the camera axis should not deviate from the

direction vertical too far, otherwise total reflection could occur

and wave effects could degrade the imaging quality. For reliable

geo-referencing and thorough verification of final data, control

points should be marked under water. This recommendation is

valid for both laser scanning and multimedia photogrammetry.

Further research should be invested for automatic extraction of

shore line from images. From differences in brightness and

colours (see Figure 9) an automatic identification of the

borderline between dry and wet areas are feasible (Kroehnert et

al., 2017 and Mulsow et al., 2014). In case of calm water bodies,

the shore line should be horizontal. This fact could be used as a

constraint in the bundle block adjustment. Highest degrees of

automation are to be expected using multispectral data including

infrared channels due to the high absorption rate of water. This

successfully demonstrated both images (Sivagami and Jayanthi,

2019) and laser scans (Morsy et al., 2018).

ACKNOWLEDGEMENTS

The investigations by Gottfried Mandlburger were supported by

the German Research Foundation (DFG) within the project

‘Bathymetry by Fusion of Airborne Laser Scanning and Multi-

Spectral Aerial Imagery’

REFERENCES

Besl, P. J., McKay, N. D., 1992. Method for registration of 3-d

shapes. In: Robotics-DL tentative, International Society for

Optics and Photonics, pp. 586–606.

Glira,, P., Pfeifer, N., Briese, C., Ressl, C., 2015. Rigorous Strip

Adjustment of Airborne Laserscanning Data Based on the Icp

Algorithm, ISPRS Annals of Photogrammetry, Remote Sensing

and Spatial Information Sciences, II-3,73-80.

Glira, P., Pfeifer, N., Mandlburger, G., 2016. Rigorous Strip

Adjustment of UAV-based Laserscanning Data Including Time-

Dependent Correction of Trajectory Errors. Photogrammetric

Engineering & Remote Sensing, 82(12), 945-954.

Gruen, A. W., 1985. Adaptive least-squares correlation –a

powerful image matching technique. South African Journal of

Photogrammetry, Remote Sensing and Cartography, 14 (3): S.

175-187.

Guenther, G.C., Cunningham, A.G., Laroque, P.E., Reid, D.J.,

2000. Meeting the accuracy challenge in airborne lidar

bathymetry. In: Proceedings of the 20th EARSeL Symposium:

Workshop on Lidar Remote Sensing of Land and Sea, Dresden.

Harris, C., Stephens, M., 1988. A Combined Corner and Edge

Detector. In Proceedings of the 4th Alvey Vision Conference,

Manchester, 31 August-2 September 1988, 147-151.

Kinzel, P.J.; Legleiter, C.J., Nelson, J.M., 2013. Mapping River

Bathymetry with a small Footprint Green LiDAR: Applications

and Challenges. JAWRA J. Am. Water Resour. Assoc. 2013, 49,

183–204.

Kröhnert, M., Meichsner, R., 2017. Segmentation of

environmental time lapse image sequences for the determination

of shore lines captured by hand-held smartphone cameras. In:

ISPRS Annals of the Photogrammetry, Remote Sensing and

Spatial In-formation Sciences IV-2/W4 (2017), S. 1–8

Maas, H.-G., 2015. On the Accuracy Potential in Underwater/

Multimedia Photogrammetry. Sensors 15(8), pp. 18140–18152.

Mandlburger, G., Pfennigbauer, M., Pfeifer, N., 2013. Analyzing

near water surface penetration in laser bathymetry—A case study

at the River Pielach. ISPRS Annals of the Photogrammetry,

Remote Sensing and Spatial Information Sciences, II-5/W2, 175-

180.

Mandlburger, G., Wenzel, K., Spitzer, A., Haala, N., Glira, P.,

Pfeifer, N., 2017. Improved Topographic Models via Concurrent

Airborne LIDAR and Dense Image Matching, ISPRS Annals of

Photogrammetry, Remote Sensing and Spatial Information

Sciences, Volume IV-2/W4, 259-266

Mandlburger, G., Kremer, J., Steinbacher, D., Baran, R., 2018.

Investigating the use of coastal blue imagery for bathymetric

mapping of inland water bodies. ISPRS - International Archives

of the Photogrammetry, Remote Sensing and Spatial Information

Sciences. XLII-1. 275-282. 10.5194/isprs-archives-XLII-1-275-

2018.

Mandlburger, G., 2018. A Case Study on Through-Water Dense

Image Matching. Int. Arch. Photogramm. Remote Sens. Spatial

Inf. Sci., XLII-2, 659-666.

Morsy, S., Shaker, A., El-Rabbany, A., 2018.Using Multispectral

Airborne LiDAR Data for Land/Water Discrimination: A Case

Study at Lake Ontario, Canada. Applied Sciences, 8(3), 1-21.

Mulsow, C., 2010. A flexible multi-media bundle approach.

International Archives of Photogrammetry, Remote Sensing and

Spatial Information Sciences, Vol. XXXVIII, Part 5 Commission

V Symposium, Newcastle upon Tyne, UK.

Mulsow, C., Maas, H.-G., 2014. A universal approach for

geometric modelling in underwater stereo image processing.

Computer Vision for Analysis of Underwater Imagery (CVAUI),

ICPR Workshop, Stockholm 2014

Mulsow, C., Koschitzki, R., Maas, H.-G., 2014.

Photogrammetric monitoring of glacier margin lakes. Geomatics,

Natural Hazards and Risk

Mulsow, C., 2018. Digital elevation models of underwater

structures from UAV imagery., Journal of Applied Hydrography

(2018), Nr. HN110, 14–19

Pfennigbauer, M., Wolf, C., Weinkopf, J., Ullrich, A., 2014.

Online waveform processing for demanding target situations. In

Proc. SPIE, 90800J.



676

Pfeifer, N., Mandlburger, G., Otepka, J., Karel, W., 2014.

OPALS – a framework for airborne laser scanning data analysis.

Computers, Environment and Urban Systems 45, 125– 136.

Pfeifer, N., Mandlburger, G., Glira, P., 2015. Laserscanning. In

C. Heipke (Ed.), Photogrammetrie und Fernerkundung (1st ed.,

pp. 1–51). Berlin Heidelberg: Springer.

Quan, X., Fry, E., 1995. Empirical equation for the index of

refraction of seawater, Appl. Opt. 34, 3477-3480

Ressl, C., Kager, H., Mandlburger, G., 2008. Quality checking of

ALS projects using statistics of strip differences. International

Archives of Photogrammetry, Remote Sensing and Spatial

Information Sciences, Vol XXXVII, 253-260.

Sivagami, K.P., Jayanthi, S.K., 2019. Automatic Water Body

Extraction using Multispectral Thresholding. International

Journal of Recent Technology and Engineering (IJRTE), 7(5S3),

254-260.

Song, Y., Niemeyer, J., Ellmer, W., Soergel, U., Heipke, C.,

2015. Comparison of three airborne laser bathymetry data sets for

monitoring the German Baltic Sea Coast.Proc.SPIE, 9638, 1-9.

Wozencraft, J. M., Lillycrop, W. J., 2003. SHOALS Airborne

Coastal Mapping: Past, Present, and Future. Journal of Coastal

Research, 2003, pp. 207–215. JSTOR,

www.jstor.org/stable/25736607. Accessed 31 Jan. 2020.



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